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Application of signal processing to respiratory cycle related EEG change (RCREC) in children

Application of signal processing to respiratory cycle related EEG change (RCREC) in children
Application of signal processing to respiratory cycle related EEG change (RCREC) in children
Sleep is an important part of everyday life. It directly affects daytime cognition and general performance. In children, sleep is a crucial requirement for growth and learning and lack of sleep may manifest itself as a long lasting developmental deficit. Sleep disorders which disrupt the normal continuity of sleep therefore benefit from early identification and treatment. A common cause of sleep disruption is sleep disordered breathing which can be associated with frequent arousals from sleep. Many relevant areas of sleep research continue to generate new and interesting findings utilising biosignals such as EEGs. Respiratory cycle related EEG change (RCREC) is a good example of this. The method for quantification of RCREC relies on the appropriate application of signal processing and the signals involved in the procedure are polysomnographic. Furthermore, RCREC is thought to reflect morbid micro-arousals in sleep and is hence also of clinical importance. Given that the field of RCREC research is a recently established one, there is much room for constructive investigation. The current state of RCREC research is therefore expanded in this thesis. The method for calculation of respiratory cycle related EEG change (RCREC) is replicated and expanded in this project. Shortcomings of the method have been identified and accounted for where appropriate. In particular, the sensitivity of RCREC to airflow signal segmentation is addressed and alternative segmentation approaches are suggested. The general influence of airflow segmentation on RCREC is investigated and a mathematical explanation for RCREC sensitivity is given. Additionally, the ability of RCREC related parameters to predict daytime cognitive functions is assessed. Results suggest that RCREC parameters are capable of predicting quality of episodic memory, power (speed) of attention and internal processing speed.
Motamedi Fakhr, Shayan
0244c29f-e17f-45ad-9a5f-4f4b96158195
Motamedi Fakhr, Shayan
0244c29f-e17f-45ad-9a5f-4f4b96158195
Moshrefi-Torbati, Mohamed
65b351dc-7c2e-4a9a-83a4-df797973913b
Hill, Martyn
0cda65c8-a70f-476f-b126-d2c4460a253e
Hill, Catherine
867cd0a0-dabc-4152-b4bf-8e9fbc0edf8d

Motamedi Fakhr, Shayan (2014) Application of signal processing to respiratory cycle related EEG change (RCREC) in children. University of Southampton, Faculty of Engineering and the Environment, Doctoral Thesis, 223pp.

Record type: Thesis (Doctoral)

Abstract

Sleep is an important part of everyday life. It directly affects daytime cognition and general performance. In children, sleep is a crucial requirement for growth and learning and lack of sleep may manifest itself as a long lasting developmental deficit. Sleep disorders which disrupt the normal continuity of sleep therefore benefit from early identification and treatment. A common cause of sleep disruption is sleep disordered breathing which can be associated with frequent arousals from sleep. Many relevant areas of sleep research continue to generate new and interesting findings utilising biosignals such as EEGs. Respiratory cycle related EEG change (RCREC) is a good example of this. The method for quantification of RCREC relies on the appropriate application of signal processing and the signals involved in the procedure are polysomnographic. Furthermore, RCREC is thought to reflect morbid micro-arousals in sleep and is hence also of clinical importance. Given that the field of RCREC research is a recently established one, there is much room for constructive investigation. The current state of RCREC research is therefore expanded in this thesis. The method for calculation of respiratory cycle related EEG change (RCREC) is replicated and expanded in this project. Shortcomings of the method have been identified and accounted for where appropriate. In particular, the sensitivity of RCREC to airflow signal segmentation is addressed and alternative segmentation approaches are suggested. The general influence of airflow segmentation on RCREC is investigated and a mathematical explanation for RCREC sensitivity is given. Additionally, the ability of RCREC related parameters to predict daytime cognitive functions is assessed. Results suggest that RCREC parameters are capable of predicting quality of episodic memory, power (speed) of attention and internal processing speed.

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More information

Published date: February 2014
Organisations: University of Southampton, Mechatronics

Identifiers

Local EPrints ID: 363767
URI: http://eprints.soton.ac.uk/id/eprint/363767
PURE UUID: 5585840c-e9e2-4b71-bbc0-8c1e018a3516
ORCID for Martyn Hill: ORCID iD orcid.org/0000-0001-6448-9448
ORCID for Catherine Hill: ORCID iD orcid.org/0000-0003-2372-5904

Catalogue record

Date deposited: 10 Apr 2014 14:08
Last modified: 27 May 2019 00:36

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Contributors

Author: Shayan Motamedi Fakhr
Thesis advisor: Mohamed Moshrefi-Torbati
Thesis advisor: Martyn Hill ORCID iD
Thesis advisor: Catherine Hill ORCID iD

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